27-03-2012, 12:04 PM
INTELLIGENT TRAFFIC LIGHT CONTROLUSING EMBEDDED SYSTEMS
10.1.1.1.5180.pdf (Size: 288.72 KB / Downloads: 130)
Introduction
Transportation research has the goal to optimize transportation flow of people and goods.
As the number of road users constantly increases, and resources provided by current infrastructures
are limited, intelligent control of traffic will become a very important issue in the
future. However, some limitations to the usage of intelligent traffic control exist. Avoiding
traffic jams for example is thought to be beneficial to both environment and economy, but
improved traffic-flow may also lead to an increase in demand [Levinson, 2003].
There are several models for traffic simulation. In our research we focus on microscopic
models that model the behavior of individual vehicles, and thereby can simulate dynamics
of groups of vehicles. Research has shown that such models yield realistic behavior
[Nagel and Schreckenberg, 1992, Wahle and Schreckenberg, 2001].
Modelling and Controlling Traffic
In this section, we focus on the use of information technology in transportation. A lot of
ground can be gained in this area, and Intelligent Transportation Systems (ITS) gained interest
of several governments and commercial companies [Ten-T expert group on ITS, 2002,
White Paper, 2001, EPA98, 1998].
ITS research includes in-car safety systems, simulating effects of infrastructural changes,
route planning, optimization of transport, and smart infrastructures. Its main goals are:
improving safety, minimizing travel time, and increasing the capacity of infrastructures. Such
improvements are beneficial to health, economy, and the environment, and this shows in the
allocated budget for ITS.
Modelling Traffic
Traffic dynamics bare resemblance with, for example, the dynamics of fluids and those of sand
in a pipe. Different approaches to modelling traffic flow can be used to explain phenomena
specific to traffic, like the spontaneous formation of traffic jams. There are two common
approaches for modelling traffic; macroscopic and microscopic models.
Macroscopic models.
Macroscopic traffic models are based on gas-kinetic models and use equations relating traffic
density to velocity [Lighthill and Whitham, 1955, Helbing et al., 2002]. These equations can
be extended with terms for build-up and relaxation of pressure to account for phenomena like
stop-and-go traffic and spontaneous congestions [Helbing et al., 2002, Jin and Zhang, 2003,
Broucke and Varaiya, 1996]. Although macroscopic models can be tuned to simulate certain
driver behaviors, they do not offer a direct, flexible, way of modelling and optimizing them,
making them less suited for our research.
Microscopic models.
In contrast to macroscopic models, microscopic traffic models offer a way of simulating various
driver behaviors. A microscopic model consists of an infrastructure that is occupied by a set
of vehicles. Each vehicle interacts with its environment according to its own rules. Depending
on these rules, different kinds of behavior emerge when groups of vehicles interact.
Vehicle Control
It is a well-known fact that traffic flow would increase drastically if all drivers would drive
at the same (maximum) speed. Another fact is that this will never happen if you let drivers
decide. In this section we first show how vehicles could learn to cooperate. We then describe
an ambitious research program that aims to control all vehicles by on-board computers.
10.1.1.1.5180.pdf (Size: 288.72 KB / Downloads: 130)
Introduction
Transportation research has the goal to optimize transportation flow of people and goods.
As the number of road users constantly increases, and resources provided by current infrastructures
are limited, intelligent control of traffic will become a very important issue in the
future. However, some limitations to the usage of intelligent traffic control exist. Avoiding
traffic jams for example is thought to be beneficial to both environment and economy, but
improved traffic-flow may also lead to an increase in demand [Levinson, 2003].
There are several models for traffic simulation. In our research we focus on microscopic
models that model the behavior of individual vehicles, and thereby can simulate dynamics
of groups of vehicles. Research has shown that such models yield realistic behavior
[Nagel and Schreckenberg, 1992, Wahle and Schreckenberg, 2001].
Modelling and Controlling Traffic
In this section, we focus on the use of information technology in transportation. A lot of
ground can be gained in this area, and Intelligent Transportation Systems (ITS) gained interest
of several governments and commercial companies [Ten-T expert group on ITS, 2002,
White Paper, 2001, EPA98, 1998].
ITS research includes in-car safety systems, simulating effects of infrastructural changes,
route planning, optimization of transport, and smart infrastructures. Its main goals are:
improving safety, minimizing travel time, and increasing the capacity of infrastructures. Such
improvements are beneficial to health, economy, and the environment, and this shows in the
allocated budget for ITS.
Modelling Traffic
Traffic dynamics bare resemblance with, for example, the dynamics of fluids and those of sand
in a pipe. Different approaches to modelling traffic flow can be used to explain phenomena
specific to traffic, like the spontaneous formation of traffic jams. There are two common
approaches for modelling traffic; macroscopic and microscopic models.
Macroscopic models.
Macroscopic traffic models are based on gas-kinetic models and use equations relating traffic
density to velocity [Lighthill and Whitham, 1955, Helbing et al., 2002]. These equations can
be extended with terms for build-up and relaxation of pressure to account for phenomena like
stop-and-go traffic and spontaneous congestions [Helbing et al., 2002, Jin and Zhang, 2003,
Broucke and Varaiya, 1996]. Although macroscopic models can be tuned to simulate certain
driver behaviors, they do not offer a direct, flexible, way of modelling and optimizing them,
making them less suited for our research.
Microscopic models.
In contrast to macroscopic models, microscopic traffic models offer a way of simulating various
driver behaviors. A microscopic model consists of an infrastructure that is occupied by a set
of vehicles. Each vehicle interacts with its environment according to its own rules. Depending
on these rules, different kinds of behavior emerge when groups of vehicles interact.
Vehicle Control
It is a well-known fact that traffic flow would increase drastically if all drivers would drive
at the same (maximum) speed. Another fact is that this will never happen if you let drivers
decide. In this section we first show how vehicles could learn to cooperate. We then describe
an ambitious research program that aims to control all vehicles by on-board computers.